Wealth, Health, and Gender: A Global Look at Adolescent Inactivity and Child Deprivation

Spring 2025 BAA1030 Data Analytics & Story Telling (20074)

Introduction

This dashboard explores the interconnectedness of economic prosperity, adolescent health, and gender inequality around the world. Using real-world data from UNICEF, we uncover how wealth shapes — but does not fully determine — childhood deprivation and physical inactivity patterns, especially among adolescent girls. The story progresses from highlighting the worst-affected countries to analyzing long-term trends and gender disparities.

# Imports
import polars as pl
import pandas as pd
from plotnine import *
import matplotlib.pyplot as plt
import geopandas as gpd
import plotly.express as px

# Read datasets
indicator_1 = pl.read_csv('unicef_indicator_1 (1).csv').to_pandas()
indicator_2 = pl.read_csv('unicef_indicator_2 (1).csv').to_pandas()
metadata = pl.read_csv('unicef_metadata (1).csv', infer_schema_length=10000, try_parse_dates=True, schema_overrides={"Population, total": pl.Float64}).to_pandas()

# Preprocessing steps

## 1️⃣ Top 10 countries with highest female adolescent inactivity
inactivity_female = indicator_1[indicator_1['sex'] == "Female"]
inactivity_latest = inactivity_female.sort_values('time_period').groupby('country').last().reset_index()
top10_inactive = inactivity_latest.sort_values('obs_value', ascending=False).head(10)

## 2️⃣ Deprivation vs GDP scatter data
deprivation_recent = indicator_2[indicator_2['sex'] == "Total"].sort_values('time_period').groupby('country').last().reset_index()
deprivation_gdp = deprivation_recent.merge(
    metadata[['country', 'year', 'GDP per capita (constant 2015 US$)']],
    left_on=['country', 'time_period'],
    right_on=['country', 'year'],
    how='inner'
).dropna(subset=['GDP per capita (constant 2015 US$)', 'obs_value'])
deprivation_gdp.rename(columns={'GDP per capita (constant 2015 US$)': 'gdp_per_capita'}, inplace=True)

## 3️⃣ Time series data
selected_countries = ["India", "Brazil", "United States"]
time_series_data = indicator_1[
    (indicator_1['country'].isin(selected_countries)) &
    (indicator_1['sex'] == "Female")
]

## 4️⃣ Gender boxplot data
inactivity_latest_gender = indicator_1.sort_values('time_period').groupby(['country', 'sex']).last().reset_index()
inactivity_latest_gender = inactivity_latest_gender.dropna(subset=['obs_value'])

1. Top 10 Countries with Female Adolescent Inactivity

“Where Girls Are Falling Behind: The Global Hotspots of Adolescent Inactivity”

Insight A small group of countries faces alarmingly high rates of female adolescent inactivity. This signals deeper social, cultural, or infrastructural challenges that limit active lifestyles for girls. Targeted interventions in these hotspots could dramatically improve global adolescent health outcomes.

Visualization

top10_plot = (
    ggplot(top10_inactive, aes(x='reorder(country, obs_value)', y='obs_value'))
    + geom_bar(stat='identity', fill='skyblue')
    + coord_flip()
    + labs(
        title='Countries with Highest Female Adolescent Inactivity',
        x='Country',
        y='Inactivity (%)'
    )
    + theme_minimal()
    + theme(
        figure_size=(10, 6),
        plot_title=element_text(size=16, weight='bold', ha='center'),
        axis_title_x=element_text(size=14),
        axis_title_y=element_text(size=14),
        axis_text_x=element_text(size=12),
        axis_text_y=element_text(size=12)
    )
)
top10_plot

2. Relationship between GDP per Capita and Child Deprivation

“Does More Money Mean Better Childhoods? The Complex Link Between Wealth and Deprivation”

Insight Wealth alone does not guarantee lower child deprivation — though a general negative trend exists. Some countries outperform or underperform relative to their income, revealing important policy lessons.

Visualization

scatter_plot = (
    ggplot(deprivation_gdp, aes(x='gdp_per_capita', y='obs_value'))
    + geom_point(color='tomato')
    + geom_smooth(method='lm', se=False, color='black')
    + labs(
        title='Child Deprivation vs GDP per Capita',
        x='GDP per capita (2015 US$)',
        y='Deprivation (%)'
    )
    + theme_minimal()
)
scatter_plot

3. Trends in Female Adolescent Inactivity for Selected Countries

“Progress or Plateau? Tracking Girls’ Inactivity Rates Over Time”

Insight India, Brazil, and the United States show different trajectories in reducing female adolescent inactivity.

Visualization

time_series_plot = (
    ggplot(time_series_data, aes(x='time_period', y='obs_value', color='country'))
    + geom_line() + geom_point()
    + labs(
        title='Adolescent Inactivity Trends in Selected Countries',
        x='Year',
        y='Inactivity (%)'
    )
    + theme_minimal()
)
time_series_plot

4. Gender Comparison in Adolescent Inactivity

“Mind the Gap: How Gender Shapes Physical Inactivity Among Adolescents”

Insight Adolescent girls consistently report higher inactivity rates than boys, highlighting the need for girl-focused initiatives.

Visualization

box_plot = (
    ggplot(inactivity_latest_gender, aes(x='sex', y='obs_value', fill='sex'))
    + geom_boxplot()
    + scale_fill_manual(values={
        'Female': 'skyblue',
        'Male': 'red',
        'Total': 'purple'
    })
    + labs(
        title='Comparison of Adolescent Inactivity by Gender and Total',
        x='Gender Category',
        y='Inactivity (%)'
    )
    + theme_minimal()
)
box_plot

5. Global Distribution of Child Deprivation (Globe View)

“Mapping Child Deprivation Around the Globe”

Insight Child deprivation clusters heavily in regions like Sub-Saharan Africa and South Asia.

Visualization

Global Child Deprivation Distribution

Conclusion

• This analysis examines global patterns of adolescent physical inactivity and child deprivation, revealing deep inequalities across countries and genders.
• Top 10 countries with the highest female inactivity rates highlight urgent public health needs.
• Trends and gender disparities point to necessary targeted action to close gaps worldwide.